Spatial Analysis For Conservation and Sustainability
Biodiversity
Species richness varies by orders of magnitude across the globe and so does the richness of endangered species. We are interested in both the macroecological processes causing richness patterns, and the socioeconomic processes determining where biodiversity is threatened.
Habitat connectivity is crucial for the conservation of species restricted to fragmented populations within human-dominated landscapes. However, identifying habitat connectivity for apex predators is challenging because trophic interactions between primary productivity and prey species influence both the distribution of habitats, and predator movement. Our goal was to assess habitat connectivity for Indochinese tigers (Panthera tigris) in Thailand. We quantified suitable habitat and dispersal corridors based an ensemble species distribution model that included prey distributions, primary productivity, and abiotic variables and was based on camera-trap data from 1996 to 2013 in 15 protected areas. We employed graph theory to evaluate the relative importance of habitat patches and dispersal corridors to the overall connectivity network. We found that tiger occurrence models with and without prey distributions performed well (Area Under the Curve: 0.932–0.954). However, inclusion of prey distributions significantly improved model performance (P < 0.001). Protected areas with tigers at the time of our surveys were highly isolated with high resistance to movement within the dispersal corridors, and four of them have lost their tiger populations since. Potential habitat patches outside of protected areas were also mostly isolated, but it was encouraging to find that there is ample potential habitat that tigers are not occupying. The Huai Kha Kaeng - ThungYai habitat patch and Kaeng Krachan dispersal corridor were the most important for overall habitat connectivity. Generally, integrating prey distributions into assessments of connectivity is a promising approach that can be widely applied to predict species occurrence and delineate dispersal corridors, thereby supporting conservation planning of tigers and other large carnivores.
Protected areas are cornerstones of conservation efforts worldwide. However, protected areas do not act in isolation because they are connected with surrounding, unprotected lands. Few studies have evaluated the effects of protected areas on wildlife populations inhabiting private lands in the surrounding landscapes. The lowland tapir Tapirus terrestris is the largest terrestrial mammal of the Neotropics and is categorized as Vulnerable on the IUCN Red List. It is necessary to understand the influence of landscape characteristics on the tapir’s habitat use to enable effective conservation management for this species. Our objectives were to () determine the potential distribution of the lowland tapir’s habitat in the Southern Yungas of Argentina, and () evaluate the role of protected areas and other covariates on tapir habitat use in adjacent private lands. We used records of lowland tapirs to model the species’ potential distribution and determined habitat use with occupancy modelling. Based on the covariates found to be significant in our models, we constructed predictive maps of probability of habitat use and assessed the area of potential habitat remaining for the species. Probability of habitat use was higher in the vicinity of two national parks and small households than further away from them. We found that in % of the lowland tapir’s potential distribution the probability of habitat use is high (..). These areas are near the three national parks in the study area. The probability of detecting lowland tapirs increased with distance to roads. We conclude that national parks play a key role in the persistence of lowland tapir populations on adjacent private lands.
Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidarbased canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidarbased canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.
Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018–2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest–nonforest in areas where the lack of detailed ecological field data precludes tree species–level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.
During the breeding season, Kirtland’s Warblers (Setophaga kirtlandii) are strongly associated with young jack pine (Pinus banksiana) forests in northern Lower Michigan, USA. Since 2007, the species has been breeding in unusual habitat, red pine (Pinus resinosa) dominated plantations, in central Wisconsin, USA. Kirtland’s Warbler productivity and habitat use in red pine is not well understood, and the central Wisconsin population is at a range edge, a situation often associated with lower productivity. To compare range-edge and range-core populations, we estimated reproductive success and characterized habitat use of Kirtland’s Warblers in central Wisconsin red pine-dominated plantations during 2015–2017 using logistic regression models. We also monitored nests and fledgling success, and estimated nest survival using logistic exposure models. Trees were closer together and herbaceous vegetation was taller and denser within territories than at randomly located points outside of territories. Females selected nest sites with deeper dead ground vegetation and live vegetation that was taller and denser than was available at randomly located points within male territories. Nest success was not strongly influenced by within-patch habitat factors. Nest daily survival rate was 0.97 (95% CI = 0.94–0.98). The average number of young fledged per nest was between 2.5 and 2.8. Nest parasitism by Brown-headed Cowbirds (Molothrus ater) was 22.7%. Overall, reproductive success in the peripheral central Wisconsin breeding population of Kirtland’s Warblers that used red pine-dominated plantations was similar to that of Kirtland’s Warblers breeding in typical jack pine habitat in the range core. Young red pine-dominated habitat appears to approximate young jack pine in habitat quality for Kirtland’s Warblers, and this may provide managers some flexibility in habitat maintenance for this conservation-reliant species.
The niche-based argument that species are filtered from environments in which they cannot sustain viable populations is the basis of the Richness-Heterogeneity Relationship (RHR). However, the multi-dimensionality of niches suggests that the RHR may take different shapes along different environmental axes, with potential confounding effects if filtering along the axes is not equally strong. Here, we explore how different structural and landscape variables drive the RHR as the accumulative outcome of environmental preferences at the species-level while considering the intercorrelation between heterogeneity levels along three niche axes. We used occurrence data of avifauna from 226 sites situated along a grassland-to-woodland gradient in a Midwestern USA study area. In each site, we quantified horizontal (habitat cover type), vertical (vegetation height structure), and spatial (habitat configuration) heterogeneity and explored the shape of the observed RHR at the landscape scale, as well as the correlations among heterogeneity levels at different axes. We then fitted species distribution models to environmental variables from the three axes separately and compared the stacked probabilities of occurrences of all species to the observed species richness. We found that predictions of richness patterns improved when more than one heterogeneity axis was included in RHR models, and that habitat suitability along different axes is not equally strong. Furthermore, a unimodal RHR along the vegetation height axis, which the species distribution models revealed to be a weak predictor for most species, may arise through intercorrelation with heterogeneity along the two other axes, along which we recorded stronger signals of environmental preference at the species level. Our results emphasize the importance of selecting relevant niche axes in studies of species richness patterns because ultimately, these patterns reflect the various environmental preferences of individual species.
Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available
energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity
(GPP), which can be estimated from satellite data.
Our question was how temporal dynamics in GPP affect bird species richness. Specifically, we evaluated the
potential of the Dynamic Habitat Indices (DHIs) derived from MODIS GPP data together with environmental and
climatic variables to explain annual patterns in bird richness across the conterminous United States. By focusing
on annual DHIs, we expand on previous applications of multi-year composite DHIs, and could evaluate lag-effects
between changes in GPP and species richness.
We used 8-day GPP data from 2003 to 2013 to calculate annual DHIs, which capture three aspects of vegetation
productivity: (1) annual cumulative productivity, (2) annual minimum productivity, and (3) annual
seasonality expressed as the coefficient of variation in productivity. For each year from 2003 to 2013, we
calculated total bird species richness and richness within six functional guilds, based on North American
Breeding Bird Survey data.
The DHIs alone explained up to 53% of the variation in annual bird richness within the different guilds
(adjusted deviance-squared D2adj = 0.20–0.52), and up to 75% of the variation (D2adj = 0.28–0.75) when
combined with other environmental and climatic variables. Annual DHIs had the highest explanatory power for
habitat-based guilds, such as grassland (D2adj = 0.67) and woodland breeding species (D2adj = 0.75). We found
some inter-annual variability in the explanatory power of annual DHIs, with a difference of 5–7 percentage
points in explained variation among years in DHI-only models, and 3–7 points for models combining DHI,
environmental and climatic variables. Our results using lagged year models did not deviate substantially from
same-year annual models.
We demonstrate the relevance of annual DHIs for biodiversity science, as effective predictors of temporal
variation in species richness patterns. We suggest that the use of annual DHIs can improve conservation planning,
by conveying the range of patterns of biodiversity response to global changes, over time.
Protected areas safeguard biodiversity and provide opportunities for human recreation. However, abundant anthropogenic food subsidies associated with human activities in protected areas can lead to high densities of generalist predators, posing a threat to rare species at broad spatial scales. Reducing anthropogenic subsidies could curb populations of overabundant predators, yet the effectiveness of this strategy is unclear. We characterized changes in the foraging ecology, body condition, and demography of a generalist predator, the Steller’s jay, three years after implementation of a multi-faceted management program to reduce anthropogenic subsidies in a protected area in California. Stable isotope analysis revealed that the proportional contribution of anthropogenic foods to jay diets declined from 88% to 47% in response to management. Overlap between jay home ranges decreased after management began, while home range size, body condition, and individual fecundity remained stable. Adult density in subsidized areas decreased markedly from 4.33 (SE: ±0.91) to 0.65 (±0.20) jays/ha after the initiation of management, whereas density in unsubsidized areas that were not expected to be affected by management remained stable (0.70 ± 0.22 pre-management, 0.58 ± 0.38 post-management). Thus, the response of jays to management was density-dependent such that reduced densities facilitated the maintenance of individual body condition and fecundity. Importantly, though, jay population size and collective reproductive output declined substantially. Our study provides evidence that limiting anthropogenic subsidies can successfully reduce generalist predator populations and be part of a strategy to increase compatibility of species protection and human recreation within protected areas.
Understanding human influence on ecosystems and their services is crucial to achieve sustainable development and ensure the conservation of biodiversity. In this context, the human footprint index (HFI) represents the anthropogenic impacts on ecosystems and the natural environment. Our objective was to characterize the HFI in Southern Patagonia (Argentina) across the landscape, qualifying the differences among the main ecological areas and especially the forested landscapes. We also assessed the potential utility of HFI to identify priority conservation areas according to their wilderness quality and potential biodiversity values. We created a HFI map (scores varied from 0 representing high wilderness quality to 1 representing maximum human impact) using variables related to direct (e.g. infrastructure) and indirect (e.g. derived from economic activities) human impacts, including settlements, accessibility, oil industry, and sheep production. HFI varied significantly across the natural landscapes, being lower (0.07 0.11) in remote ecosystems close to the Andes Mountains and higher (0.38 0.40) in southern areas close to the provincial capital city. Forested landscapes presented different impact values, which were directly related to the economical values of the different forest types. We determined that the current protected area network is not equally distributed across the different ecological areas and forest types. Priority conservation areas were also identified using the fragmentation produced by the human impact, the patch size, and the potential biodiversity values. HFI can present high compatibility with other land-use management decision making tools, acting as a complement to the existing tools for conservation planning or management.
Considering their outsized importance as prey for so many species one would assume that patterns of insect abundance and their determinants have been well-studied. On the contrary, insect ecology is poorly understood and documented. Our study sought to gain an understanding of the subgroup of insects that fly, with a particular emphasis on groups that spend part of their life in lakes and streams.
Insect trapping took place in the northern highland lake district. The study area consists of northern mixed forest with one of the highest densities of lakes in the world.
We conducted insect trapping over three years in the forest landscape of northern Wisconsin, near UW-Madison’s Trout Lake Research Station. We trapped insects May-August around five different lakes and identified them in the lab.
Mean insect abundance varied over the course of the summer with peak average insect abundance occurring in late May and early June (Julian Day 145-155) in both years.
There were several patterns that stood out. Flying insects tended to be many times more abundant in nearshore areas compared to interior forests. Different groups of insects showed different patterns. Diptera, including deerflies, midges, and gnats were the most abundant insects overall. As expected, emergent aquatic groups such as midges, mayflies, and dragonflies were more abundant in nearshore areas while beetles and thrips were more abundant in forest interiors. There were also multiple peaks of abundance through the season with large emergence events of midges and mayflies driving much of the pattern. In addition, local canopy cover was negatively correlated with insect abundance.
We observed birds, bats, and fish consuming flying insects. Abundance of these insect predators likely tracks the abundance of their insect prey. In addition, insects perform other ecosystems services such as pollination and nutrient cycling. Understanding the patterns and drivers of insect abundance can help us better understand northern Wisconsin forest ecosystems.
